不同时刻地震直接人员伤亡人数的预测

在建筑物单体震害预测的基础上,从人与建筑物的相对关系出发,以建筑物容人时间分布曲线和当量人数为指标,建立了预测模型;并以大连经济技术开发区为例,给出了未来地震中昼夜平均人员伤亡和不同时刻人员恨的预测结果。     

温室气体对气候环境的影响预测及其不确定性

随着温室气体排放量的增加,联系温室气体特有的保温效应,大气平均气温将升高。在采用气候模式对未来近百年的气候变化做出一定预测的基础上,分析未来可能出现的气候变化所造成的对气候环境方面的影响。详细分析了现阶段预测未来气候环境变化存在的不确定性,正是这些不确定性影响了预测的可信度。     

BP神经网络在城市建成区面积预测中的应用——以江苏省为例

城市建成区规模的迅速扩张是目前江苏省土地利用变化中的一个显著特点，其面积从1985年的426 km2扩大到2003年的2 200 km2，平均年增加98.56 km2。城市建成区规模的扩张受到社会、经济、人口等多种因素的影响，用传统方法对其进行预测比较困难。鉴于BP神经网络在非线性领域预测中的广泛应用，以江苏省为研究对象，构建一个11-3-1结构的BP神经网络预测模型，以1985~2001年和2002年的相关数据作为模型的训练和测试样本，以2003年的社会、经济数据作为网络的预测输入，对该年的建成区面积进行预测。结果表明，BP神经网络预测结果与实际面积的相对误差为3.96%，其预测精度与多元回归预测模型相比有较大改善。     

Prediction of Annual Streambank Erosion for Sequoia National Forest,California

Many bank erosion models have limitations that restrict their use in wildland settings. Scientists and land managers at the Sequoia National Forest would like to understand the mechanisms and rates of streambank erosion to evaluate management issues and post‐wildfire effects. This study uses bank erosion hazard index (BEHI) and near‐bank stress (NBS) methods developed in Rosgen (2006 Watershed Assessment of River Stability and Sediment Supply [WARSSS]) for predicting streambank erosion in a geographic area that is dominated by colluvium and in which streambank erosion modeling has not been previously evaluated. BEHI evaluates bank susceptibility to erosion based on bank angle, bank and bankfull height, rooting depth and density, surface protection, and stratification of material within the banks. NBS assesses energy distribution against the bank measured as a ratio of bankfull near‐bank maximum depth to mean bankfull depth. We compared BEHI classes and NBS to actual bank erosion measured from 2008 to 2012. This index predicted streambank erosion with clear separation among BEHI ratings with R² values of 0.76 for extreme, 0.37 for high/very high, 0.49 for moderate, and 0.70 for low BEHI. The relationships between measured erosion and BEHI extend the application of BEHI/NBS to a new region where they can inform management priorities, afforestation, stream/riparian restoration projects, and potentially burned area rehabilitation.     

基于VMAS四川省在用点燃式轻型车尾气污染削减预测

针对四川省在用车辆尾气污染情况开展了详细的调查研究,结果表明四川省机动车尾气污染中约71.3%的CO、68.6%的HC、95.3%的NOx和99.2%的PM均来自于汽车尾气。同时分析得出简易瞬态工况法是在用车辆尾气排放控制的有效控制方法,有利于四川省大气环境质量的改善,经预测采用简易瞬态工况法以及简易瞬态工况法排放标准后可使四川省在用点燃式轻型车尾气排放的CO削减约28.76%~50.48%,HC+NOx削减约1.53%~23.24%。     

四川西南部山区风电场工程水土流失预测浅析

本文介绍了凉山州长海子风电场工程水土流失预测范围与时段、内容与方法。通过采用类比工程法进行预测,得到了水土流失预测结果,确定了风电场水土流失重点区域为交通设施区、弃渣场区,水土流失主要时段为施工期。据此预测结果,为工程在建设过程中提出了水土保持设计建议。     

风蚀性粒子降尘浓度的动力学预报模式

为探求开放源露天煤尘在自然风力作用下的扩散情况,本研究采用空气动力学的数值计算方法,以离散相拉格朗日随机轨道为计算模型,预测了原煤、大矿、水洗和精煤4类煤种料堆的降尘浓度分布,并采用风洞试验对结果进行了对比验证.结果表明:动力学模式对风蚀粒子的预报误差在±25%,其中原煤种的预报度最高,误差10%.动力学模式与高斯沉降模式相比,高斯模式对尘源处的降尘浓度预报误差过大,动力学预报模式是基于颗粒群的运动轨迹的行为预报,能准确地描述粗大颗粒的跃移运动状态,对于满足Rosin-Rammler粒径分布的固体粒子降尘浓度的预报良好.     

水电工程施工阶段应急管理系统脆弱性评价

为有效评估水电工程施工阶段应急管理系统应急能力并发现系统中的薄弱环节,提出一种应急管理系统脆弱性评价方法。首先,引入脆弱性理论,将应急管理系统的脆弱性分为暴露性、适应性和恢复性3部分;然后,根据水电工程施工阶段应急管理系统的特点构建了进攻性和防御性指标体系,并运用ANP（网络层次分析法）计算各指标权重,结合灰色聚类分析得到各防御性指标和系统整体的脆弱性系数,从而判断水电工程施工阶段应急管理系统脆弱性等级;最后,运用该模型对国内某水电站工程应急管理系统进行评价,在定量计算的基础上得到该应急系统的脆弱性等级,结果表明该系统脆弱性等级处于中等状态,并发现其薄弱环节,为完善水电工程施工阶段应急管理系统的建设提供支撑。     

基于微震监测的岩体失稳智能预报

岩体稳定性预报是微震监测工作的重要组成部分。探讨微震活动性参数变化作为岩体稳定性预测的可行性,结合用沙坝矿实际生产过程中观测到的现象,提出微震监测岩体失稳预报的一般模式。根据b值在岩体失稳前先增大后减小的趋势作为危险预警初始条件;并将能量指数、施密特数急剧下降以及累积视体积增加的时间段作为岩体失稳的预警期,将事件数的急剧下降作为危险的临界状态。用沙坝矿根据这一原理建立的岩体失稳预报模式可以达到采场失稳的智能预报,解决了人工判别预警期过程中效率低的难题,保障矿山的生产工作。     

Evaluation of the k-nearest neighbor method for forecasting the influent characteristics of wastewater treatment plant

The k-nearest neighbor (k-NN) method was evaluated to predict the influent flow rate and four water qualities, namely chemical oxygen demand (COD), suspended solid (SS), total nitrogen (T-N) and total phosphorus (T-P) at a wastewater treatment plant (WWTP). The search range and approach for determining the number of nearest neighbors (NNs) under dry and wet weather conditions were initially optimized based on the root mean square error (RMSE). The optimum search range for considering data size was one year. The square root-based (SR) approach was superior to the distance factor-based (DF) approach in determining the appropriate number of NNs. However, the results for both approaches varied slightly depending on the water quality and the weather conditions. The influent flow rate was accurately predicted within one standard deviation of measured values. Influent water qualities were well predicted with the mean absolute percentage error (MAPE) under both wet and dry weather conditions. For the seven-day prediction, the difference in predictive accuracy was less than 5% in dry weather conditions and slightly worse in wet weather conditions. Overall, the k-NN method was verified to be useful for predicting WWTP influent characteristics.